User Behavior Analytics and Software Development for Assessing RaceRunner Customers****

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Customer retention is most important to the growth of a company. It doesn't matter how much you spend to acquire new users if you can't retain them. In this project, we will develop strategies to model complex user behaviors to gain a deeper understanding of app user customers using Bayesian networks, predictive models and machine learning algorithms. Bayesian methods allow us to encode our prior beliefs about what these predictive models should look like, conditioning on what the data tell us. These methods will allow to predict the probability of leaving any new or existing customer given his/her past data. This is important for identifying the customers who are about to leave the service, based on their actions and take appropriate remedial measures. These methods will also facilitate the behavioral clustering of customers and user churn prediction in order to better understand the health of the business. The methods will be tested on over one million records of structured, unstructured and multidimensional data available from RaceRunner customers. We then aim to develop a software platform to make it usable for decision makers and customers without any analytics knowledge. The software will allow the company to analyze and predict their customer choice behavior and facilitate a better understanding of this behavior which will allow RaceRunner to introduce more successful features and services and increase their market share.****